Talk by Werner Reichardt of the Centre for Integrative Neuroscience, Tübingen. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley.

Abstract.

Probabilistic models of natural images have been used to solve a variety of computer vision tasks as well as a means to better understand the computations performed by the visual system in the brain. A lot of theoretical considerations and biological observations point to the fact that natural image models should be hierarchically organized, yet to date, the best known models are still based on what is better described as shallow representations. In this talk, I will present two image models. One is based on the idea of Gaussianization for greedily constructing hierarchical generative models. I will show that when combined with independent subspace analysis, it is able to compete with the state of the art for modeling image patches. The other model combines mixtures of Gaussian scale mixtures with a directed graphical model and multiscale image representations and is able to generate highly structured images of arbitrary size. Evaluating the model's likelihood and comparing it to a large number of other image models shows that it might well be the best model for natural images yet.

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